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      Kernel density estimation and its application

      , , ,
      ITM Web of Conferences
      EDP Sciences

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          Abstract

          Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel technique produces smooth estimate of the pdf, uses all sample points' locations and more convincingly suggest multimodality. In its two-dimensional applications, kernel estimation is even better as the 2D histogram requires additionally to define the orientation of 2D bins. Two concepts play fundamental role in kernel estimation: kernel function shape and coefficient of smoothness, of which the latter is crucial to the method. Several real-life examples, both for univariate and bivariate applications, are shown.

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          Most cited references26

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          On Estimation of a Probability Density Function and Mode

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            Density Estimation for Statistics and Data Analysis

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              On the histogram as a density estimator:L 2 theory

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                Author and article information

                Journal
                ITM Web of Conferences
                ITM Web Conf.
                EDP Sciences
                2271-2097
                2018
                November 07 2018
                2018
                : 23
                : 00037
                Article
                10.1051/itmconf/20182300037
                2a4ad3e8-0fdc-4746-8ef5-8b0867fcd940
                © 2018

                http://creativecommons.org/licenses/by/4.0

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